Assessing physical activity and sedentary behaviour in cardiac rehabilitation: Implications of using uniaxial versus vector magnitude accelerometer data
Bibliographic record
Abstract
Purpose: This study compared the minutes/day of moderate-to-vigorous physical activity (MVPA) and sedentary time (ST) using the vertical axis (uniaxial) vs. vector magnitude (VM: triaxial) counts per minute data in cardiac rehabilitation (CR) patients. Methods: Accelerometry data was collected on 46 (14 women, mean age 62) CR patients. Patients wore an accelerometer for 9 days at the beginning (i.e., within the 1st three weeks), end, and 3-months after completing CR. For the current abstract, data is only available for baseline data collection. Results: Seperate paired sample t-tests were performed to examine whether there were differences in the minutes/day of MVPA and ST calculated using the uniaxial vs. VM data, respectively. Results showed there was a significant difference in both the minutes/day of MVPA [t(43) = -14.12, p = 0.00] and ST [t(43) = 10.46, p = 0.00] using uniaxial vs. VM data. Measurement of agreement between the minutes/day of (a) MVPA for the uniaxial vs. VM, and (b) ST for the uniaxial vs. VM, were exmained using Bland-Altman plots. Results showed that the uniaxial data underestimated participants' daily time in MVPA by 24 (+ 15) minutes and overestimated ST by 72 (+ 34) minutes compared to MVPA and ST calculated using VM data. Conclusion: Utilizing either uniaxial or VM data was found to significantly impact MVPA and sedentary behaviour outcome measures in CR patients. These findings highlight the impact different accelerometer data post-processing procedures can have on outcome measures in this population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".